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Unsupervised detection of cover song sets: accuracy improvement and original identification

Title Unsupervised detection of cover song sets: accuracy improvement and original identification
Publication Type Conference Paper
Year of Publication 2009
Conference Name Conference of the International Society for Music Information Retrieval (ISMIR)
Authors Serrà, J. , Zanin M. , Laurier C. , & Sordo M.
Pagination 225-230
Conference Start Date 26/10/2009
Conference Location Kobe, Japan
ISBN Number 978-0-9813537-0-8
Abstract The task of identifying cover songs has formerly been studied in terms of a prototypical query retrieval framework. However, this framework is not the only one the task allows. In this article, we revise the task of identifying cover songs to include the notion of sets (or groups) of covers. In particular, we study the application of unsupervised clustering and community detection algorithms to detect cover sets. We consider current state-of-the-art algorithms and propose new methods to achieve this goal. Our experiments show that the detection of cover sets is feasible, that it can be performed in a reasonable amount of time, that it does not require extensive parameter tuning, and that it presents certain robustness to inaccurate measurements. Furthermore, we highlight two direct outcomes that naturally arise from the proposed framework revision: increasing the accuracy of query retrieval-based systems and detecting the original song within a set of covers.
preprint/postprint document files/publications/jserra09ismir_PS2-6.pdf